Anti-Surfer Targeting

It's 1NN with only firing waves. It seems that kd-tree is the only slow part.

Worth mention that I already store everything slow to file, e.g. precise intersection, precise mea etc. So all I do is load those attributes, transform with my formula, load into tree and do kde for every firing wave.

Anyway this can be considered as 1 population and 1 generation, as I'm tuning it by hand yet.

OK, now I understand. I was afraid that I had a big flaw in the algorithm that made it slow. What I learned is genetic algorithm always works better than manual tuning in the long run. What I do when it ends is to roll the numbers that are really high and low to the max/min values and then I get about a 1% boost in score which easily surpasses the hand tuning. Since only my GA is multi-threaded hand tuning is a little slower too.

One final question, where do the files I save go on RoboRunner-GUI? I didn't even test(Std Me) before putting WhiteFang against 28 surfers for 10 seasons then I accidentally compiled the project(Me again).

I have even tried sorting by "last modified" and "last created" but nothing seems to have appeared. I am still getting data files with the development version though. Can't test the packed one because of the bug in 1.9.3.5.

Edit: .data was a hidden directory. command + shift + . solved all the problems.

Update: after some profiling, it confirmed that kd-tree is the only bottleneck.

However, it seems that file reading time grows as kd-tree time grows.

And after putting deserializaion into separate thread and use some producer-consumer pattern to communicate, total run time stays the same and file reading time decreased greatly. Maybe my profiling tool is yielding inaccurate result.